To all the people I have copied throughout my education and professional career, I want to say thank you.

It is a great privilege to be able to learn from others. In many ways, that is the mother of all innovation.

This week, I was not copied, I was cloned. (🙄)

At first, I was salty. I felt protective. I felt like something had been taken from me.

But after sitting with it, I recognized how much of what I have created has come from the inspiration of others.

I literally keep notes, boards, folders, and screenshots full of ideas I admire. My product builds have been shaped by features I first saw somewhere else. My writing has been influenced by people whose work made me think differently. My taste has been built by paying attention to other people’s taste.

It is easy to be ungrateful. It is easy to feel like someone stole your idea.

But in the age of LLMs, the truth is becoming harder to ignore: most of us are aggregators of other people’s inventions. We remix, reframe, combine, imitate, and improve. Originality is rarely about creating from nothing. More often, it is about seeing connections others missed.

So if you have been copied, maybe there is another way to look at it.

You created something worth noticing.

You built something that drew enough attention for someone else to study it, borrow from it, and try to make something of their own from it.

That does not mean copying always feels good. It does not mean attribution does not matter. It does not mean people should take without care.

But it does mean your ideas are alive.

And if your work is inspiring enough to be copied, maybe that is not only a threat.

Maybe it is also proof.

Over and out.

P.S. Readers get free access to Resonant, which was used to dictate this article for you.

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Every big AI company, every AI lab, is becoming a product company.

A lab is just a place where you do R&D, create research, apply intelligence, and use that research to build products.

They’re moving from upstream intelligence to downstream application because of agents and integrations for brand protection, revenue opportunity, and experimentation.

Anthropic launched Claude Code, then Claude Design — a recent product coming out of the lab.

Every product gets its time to shine out of the lab before it loops back to feed model improvement data.

• OpenAI did the same: ChatGPT → Codex.

• Cognition Labs built Devin.

• Moonshot built Kimi.

• Runway opened Runway Labs.

• Google runs Google Labs.

• Gamma released an agentic product from their lab.

• Lovable runs the same shape, though theirs reads more like an experimental product surface than a declared lab division.

• Anthropic Labs is explicit about their framing.

Now flip it.

How do you design your own product so it builds the training dataset for your next model?

You’ve probably already seen it in Claude design where you have to approve or deny a specific item and give information as to why.

What does a lab-focused distribution center actually imply?

You’ve gone from model-first to experimentation-first.

Coding agents and agentic systems cleared so much headroom that big enterprises can now spin up dedicated spaces, test fast, and ship niche products or quirks — and keep the high-risk experiments away from the core business.

It’s easier to blame the lab for something that didn’t land than to risk the reputation of the firm. Case in point: the AI browsers from Google, OpenAI, and Perplexity, who all figured out they needed to remerge those browsers into super apps (or Computer’s).

From a business and investment angle, it’s just a pipeline of new products orbiting one flagship center.

A great way to experiment, find new streams inside the ecosystem, and push them to market.

Most companies have the same problem: they can’t distribute their software to market as fast as they can make it. It’s also why solo founders ship so fast. They build everything, worry about nothing, carry no internal friction, and sit directly on the problem.

In 2026, watch the word “lab” turn into a buzzword.

Treat it as a soft launch.

They’ve already run the thing internally — now they’re trying to build a category-leading product by bringing it out, often at the expense of a downstream or adjacent competitor.

There’s a second door, and almost no one is naming it.

The same friction that makes a firm build a lab makes its best people walk and rebuild the work outside, funded in months.

LeCun left Meta for AMI Labs. Goldie and Mirhoseini left Anthropic and Google for Ricursive Intelligence. Silver left DeepMind for Ineffable Intelligence.

The lab is a soft launch and a holding pen at once.

The holding pen has a door, and the venture money is parked right outside it.

Good luck.

I spoke to my friend at HatchWorks (and also a writer here on Substack) about what I’m up to with Resonant, past projects, and how building solo in the age of agents & AI has ‘changed the game’.

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Most people think tech luxury looks like a new MacBook. It doesn’t.

Tech luxury is quieter than that.

It’s the absence of friction in your day.

It’s the small subscriptions stacked on top of each other that nobody notices exc…

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After a few months of running both heavy, here’s what actually shook out — and why I now route work like a switchboard operator instead of a loyal customer.

My daily drivers are three: Opus 4.7, GPT-5.5, and SWE 1.6 from Cognition. Second tier is Kimi K2.6. Everything else, I don’t touch much right now…

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This is the first piece in a series I’m calling Lazy Leverage. Five skills worth learning now.

Read it slowly.

If you read this newsletter, you’re past the AI-skeptic phase. Your boss is pushing it. Your team is adopting it. The argument about whether to use it is over.

The interesting question is what you do once you’ve arrived at this point.

Here’s the frame I’d offer:

Humans are the last mile of LLMs.

Last mile, in logistics, means the final delivery stretch. The expensive part. The part that doesn’t automate. That’s the position humans now hold relative to language models. The model handles the bulk transit. You handle the last few feet.

The last mile is the quality layer. It’s the orchestration. It’s the refinements that other humans relate to because a human shaped them. Models can produce volume. They can’t yet produce the finish that makes another person trust the work. That gap is the job.

There’s a related shift worth naming. Software engineering has always had a property most professions lack: all related work context arrives upfront, you research, test, loop.

AI is what gives every other profession that same property. Marketing, sales, operations, legal — none of them currently have that loop. AI makes it possible by pulling context together, triaging events, taking action across tools.

Which means software itself is changing shape. The pattern emerging looks like three layers: interfaces, language models, and skills. The interface is how a human or another agent reaches in. The model is the engine. Skills — markdown files containing instructions, references, and procedures — are how the model knows what to do in your specific context.

Skills can be better than scripts because they’re self-healing.

A script does only what you told it. When the world changes, the script breaks. A skill is different. The skill says what. The agent reading the skill figures out how under current conditions.

The agent brings judgment the code can’t. That’s why skills compound while scripts depreciate — the script is locked to the world it was written in, the skill navigates whatever challenge is current.

The five skills below describe what the last mile looks like in practice.

People are becoming architects and orchestrators. These are the skills of that role.

1. Managing morphing systems

The skill: designing systems you expect to remake. Building structures with the assumption that the tools, models, and inputs underneath them will change before the structure does — and treating that ‘churn’ as a feature, not a failure.

Most knowledge work is a chain of triggers. A thing arrives — a ticket, an email, a PDF, a Slack message — and you run a procedure on it. Sometimes the procedure is a checklist in your head. Sometimes it’s an automation. Sometimes it’s a handoff.

AI keeps breaking those procedures. A new model lands. A new tool ships. Last month’s system is now wrong, and the system you build next month will also be wrong, on a shorter timeline.

The skill is being willing to repurpose what you built without grief.

That’s harder than it sounds. People get attached to work, especially work they did well. The morphing-systems mindset asks you to treat last quarter’s pipeline as parts, not architecture. The structure can be rebuilt. The pieces inside it carry forward.

This sets up a tension that skill five resolves: how do you stay willing to remake systems while also building things that compound? The answer is that the structure morphs. The pieces inside it — the prompts, the skill files, the workflows — don’t. We’ll come back to that.

📌 Try this week: pick one workflow you’ve built. Ask “what would change if a new model could do 80% of it natively”. Don’t act on the answer. Just notice how attached you are to the existing version.

2. Orchestrating agents

The skill: directing multiple AI agents working in parallel toward a shared outcome. Giving each one a clear brief, watching their outputs, and managing the loop between them so the bottleneck stays on direction rather than execution.

Codex, Claude Code / Cowork, Devin — coding agents have gotten so good at programmatic efforts and reasoning (coding), they’ve now crossed into operational work. Context windows keep expanding. Models keep getting smarter. The agents will keep absorbing more of the day.

The skill is running several at once. Five panels, five tasks, you on top.

This sounds counterproductive for deep focus. It is, until you know your objective well enough to delegate cleanly. Then it stops feeling like multitasking and starts feeling like management. The bottleneck moves from execution to direction.

Direction is harder than people expect. Most of the difficulty in delegating to a junior employee is the same difficulty that shows up here, just compressed. Vague brief, vague output.

Specific brief (with real examples) = useful output.

A working definition: an agent is an LLM + tools + an environment.

The environment can be your laptop or a server somewhere — it matters less than people think.

What matters is the loop:

• How does the agent receive a task?

• What tools can it reach?

  • Can it touch or ‘see’ the right systems?

• How does its output get back to you?

📌 Try this week: line up two agents on adjacent parts of one task. Watch where you become the bottleneck. That’s where your next skill file needs work.

3. Thinking like an architect

The skill: planning the structure of a system before building any of it. Defining how the pieces fit, what each one is responsible for, and what could replace it — so that when execution gets cheap, the design is what determines the quality of the result.

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I make content for 70,000 people a week across socials running a creator business in tech and by extension…AI. But the number keeps climbing up, as does the bar for quality. In order to educate and serve at that scale, I hav…

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You maxed out Claude Code.

Or OpenAI Codex.

The cursor blinks and nothing happens.

You get the dreaded - “you’ve hit your usage limit, wait until XX:XX” message.

Stop paying for overages.

Use free inference instead.

Option 1: OpenRouter

Go to openrouter.ai. Click Rankings.

The leaderboard shows what people are actually using right now. New models land there constantly. Many are free.

Qwen, for example recently shipped free inference in exchange for coding data for their new model releases (yes even free has a price, advised only for personal projects). Others do the same.

Pick one. Sign up for an account, get an API key, copy the model ID.

Plug it into your harness.

Cursor, OpenCode as an examples.

Option 2: Nvidia (🐐)

This is the one people sleep on.

The world’s largest inference provider gives you 80 hosted models for free. Serverless. No infra. No credit card friction.

That should tell you something about where the market is going.

Setup takes four minutes

1. Create an account. Go to build.nvidia.com. Sign up.

2. Open API Keys. Create a test key. Copy it.

Go to Models. Filter by Free Endpoint.

You’ll see the good ones:

• GLM 4.7

• Nemotron

• Minimax 2.7

• GLM 5.1

• Gemma

• Devstral from Mistral

All free. All serverless. All ready.

Wire it into your editor

Works in OpenCode. Works in Cursor. Works in Conductor.

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• As a gift: Get free access to Resonant, which was used to dictate this article for you.

• Rolling out AI across your org? Supercenter plugs every enterprise system into any a…

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• Get free access to Resonant, which was used to dictate this article for you.

• Rolling out AI across your org? Supercenter plugs every enterprise system into any AI chat with a…

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Software used to be a fortress. Walls, moats, lock-in. That metaphor is dead.

The defensible software of 2026 is a living system. It grows like fungi. It spreads through ecosystems. It becomes impossible to remove — not because it traps users, but because it feeds them.

Here’s what fungi actually do. A fruiting body sheds spores. Those spores land in new environments and germinate. They send out mycelium — branching filaments that weave through soil, connecting root systems, breaking down matter, cycling nutrients. New fruiting bodies emerge. More spores. The organism doesn’t defend territory.

It becomes the territory.

Your software needs to do the same thing.

Everything is becoming AI chat now. Capabilities converge. User experience, AI experience, interface design — these become the only differentiators as the underlying technology flattens. And here’s the shift most founders haven’t internalized: the customer is no longer the human. The human is the director. The customer — the thing that actually touches your software thousands of times a day, that reads your API responses, that passes data between tools — is increasingly an agent. A workflow. An autonomous system acting on someone’s behalf.

This changes what defensibility means. So where’s the moat?

Two places. Network weaving — myceliating your product into your industry ecosystem. And building proprietary intelligence that LLMs can access but can’t absorb.

Spores: How You Spread

A spore is a reproductive cell that travels. In software, spores are the artifacts your product sheds into the wild. Backlinks. Screenshots auto-branded from X or Reddit. Shareable assets. Embeddable widgets. Exported data that carries your name.

Every time a user screenshots your product and posts it, that’s a spore. Every API response with your attribution, a spore. Every markdown export with your watermark, a spore. These land in new ecosystems — social feeds, Slack channels, blog posts, competitor comparisons — and germinate. Someone sees the spore. Traces it back. Starts using the product. Now they’re shedding spores too.

NFX draws a critical distinction: viral effects and network effects are not the same thing. Viral effects are about acquiring new users — getting existing customers to bring you more customers, ideally for free. Network effects are about defensibility — every new user adds incremental value to all existing users, making it harder for anyone to leave. Spores are the viral mechanism. What they grow into is the network effect. You need both, and you need to know which one you’re building at any given moment.

Most founders think about distribution as something that happens to the product. Fungi think about distribution as something the product does.

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👋 Each week, builders, creators, and founders get practical insights on what’s moving in tech/AI and how to actually apply it. For more: see my site | IG / TT | Twitter/X | YouTube.

P.S. Get free access to Resonant, which was used to dictate this article for you.

Read more

👋 Each week, builders, creators, and founders get practical insights on what’s moving in tech/AI and how to actually apply it. For more: see my site | IG / TT | Twitter/X | YouTube.

P.S. Get free access to Resonant, which was used to dictate this article for you.

Read more

P.S. Get free access to Resonant, which was used to dictate this article for you.

Read more

👋 Each week, builders, creators, and founders get practical insights on what’s moving in tech/AI and how to actually apply it. For more: see my site | IG / TT | Twitter/X | YouTube.

P.S. Get free access to Resonant, which was used to dictate this article for you.

Read more

P.S. Get free access to Resonant, which was used to dictate this article for you.

Read more

P.S. Get free access to Resonant, which was used to dictate this article for you.

Read more

There is a low ceiling on what you can say. Not because the AI can't handle it — but because typing is slow, and your thoughts aren't.

## Mouse and keyboard is low bandwidth

If you look at the bandwidth and density of human-computer interaction — how quickly and effectivel…

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Start with your ICP. How do they describe what your product actually does — not your marketing copy, but the way they'd explain it to a friend? Listen for the casual version, the one with no jargon.

For a voice transcription product, it might sound like: *"I just click in the box, press a button, talk, and…

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